Programming Foundations for Scientific Big Data Analytics

Big data analytics is the process of examining large data sets to uncover hidden patterns and previously unknown correlations. Big data analytics has been widely used in businesses to find market trends, customer preferences, and other useful business information. The research community is also beginning to embrace this exciting and powerful technology. Considering the huge amount of data produced in scientific fields such as biology, medicine, physics, and material science, big data analytics can be a powerful means of making new scientific discoveries. Efficient and effective big data analytics requires the development of programming tools and models. This special issue attracted 20 high quality submissions. After a rigorous review process, 13 papers were accepted in this issue. The research presented in these papers can be roughly categorized into three areas: (1) platform for big data analytics (3 papers), (2) machine learning algorithms for big data (6 papers), and (3) big data analytics for various applications (4 papers). Platform for Big Data Analytics. B. R. Chang et al. reported their work on how to integrate popular big data platforms such as Hadoop and Spark to perform high performance big data analytics. They focused on the optimization of job scheduling based on computing features to improve system throughout. L. Zhang and J. Gao introduced a novel incremental graph pattern matching algorithm for big graph data. By batching insert operations together by considering matching states, they were able to demonstrate higher efficiency of the proposed algorithm. L. Yang et al. proposed several optimization algorithms based on node compression to help solve the shortest path problem in the context of routing big data.